LiDAR confidence estimation.
Few works allow computing the confidence on the LiDAR sparse depth map itself. Indeed, most attention to confidence estimation was given to estimating the uncertainty of the depth predicted by the overall framework using the LiDAR data, as in the case of depth completion [1]}, [2]}. To the best of our knowledge, only the following works estimate the confidence on the input LiDAR sparse depth map. Eldesokey et al. [2]} were the first explicitly modeling such confidence, but this happens as a side effect of making their depth completion framework more robust versus input noise. Thus it requires a massive amount of labeled data (i.e., the whole KITTI dataset with accumulated ground-truth depth maps) to train the model for its primary task: performing depth completion. Zhao et al. [4]} propose instead a depth completion method that does not rely on learning, by using the local surface geometry of depth points, and enhance their system by employing a binary outliers detection algorithm.
Our solution differs from this latter since i) it is a learned method and ii) it generates confidence in place of a binary score, which allows for a finer outliers filtering mechanism, as we will see in our experiments.
Finally, concerning LiDAR filtering through a stereo setup, we mention the work by Cheng et al. [5]}, filtering the outliers in LiDAR depth maps while performing a noise-aware fusion with stereo data by checking consistency with the output of the fusion itself.
